Toyota

Aygo

1.0

790

99

Mitsubishi

Space Star

1.2

1160

95

Skoda

Citigo

1.0

929

95

Fiat

500

0.9

865

90

Mini

Cooper

1.5

1140

105

VW

Up!

1.0

929

105

Skoda

Fabia

1.4

1109

90

Mercedes

A-Class

1.5

1365

92

Ford

Fiesta

1.5

1112

98

Audi

A1

1.6

1150

99

Hyundai

I20

1.1

980

99

Suzuki

Swift

1.3

990

101

Ford

Fiesta

1.0

1112

99

Honda

Civic

1.6

1252

94

Hundai

I30

1.6

1326

97

Opel

Astra

1.6

1330

97

BMW

1

1.6

1365

99

Mazda

3

2.2

1280

104

Skoda

Rapid

1.6

1119

104

Ford

Focus

2.0

1328

105

Ford

Mondeo

1.6

1584

94

Opel

Insignia

2.0

1428

99

Mercedes

C-Class

2.1

1365

99

Skoda

Octavia

1.6

1415

99

Volvo

S60

2.0

1415

99

Mercedes

CLA

1.5

1465

102

Audi

A4

2.0

1490

104

Audi

A6

2.0

1725

114

Volvo

V70

1.6

1523

109

BMW

5

2.0

1705

114

Mercedes

E-Class

2.1

1605

115

Volvo

XC70

2.0

1746

117

Ford

B-Max

1.6

1235

104

BMW

2

1.6

1390

108

Opel

Zafira

1.6

1405

109

Mercedes

SLK

2.5

1395

120

z = (x - u) / s

import pandas
from sklearn import linear_model
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()
df = pandas.read_csv("cars2.csv")
X = df[['Weight', 'Volume']]
scaledX = scale.fit_transform(X)
print(scaledX)

[[-2.10389253 -1.59336644]
 [-0.55407235 -1.07190106]
 [-1.52166278 -1.59336644]
 [-1.78973979 -1.85409913]
 [-0.63784641 -0.28970299]
 [-1.52166278 -1.59336644]
 [-0.76769621 -0.55043568]
 [ 0.3046118  -0.28970299]
 [-0.7551301  -0.28970299]
 [-0.59595938 -0.0289703 ]
 [-1.30803892 -1.33263375]
 [-1.26615189 -0.81116837]
 [-0.7551301  -1.59336644]
 [-0.16871166 -0.0289703 ]
 [ 0.14125238 -0.0289703 ]
 [ 0.15800719 -0.0289703 ]
 [ 0.3046118  -0.0289703 ]
 [-0.05142797  1.53542584]
 [-0.72580918 -0.0289703 ]
 [ 0.14962979  1.01396046]
 [ 1.2219378  -0.0289703 ]
 [ 0.5685001   1.01396046]
 [ 0.3046118   1.27469315]
 [ 0.51404696 -0.0289703 ]
 [ 0.51404696  1.01396046]
 [ 0.72348212 -0.28970299]
 [ 0.8281997   1.01396046]
 [ 1.81254495  1.01396046]
 [ 0.96642691 -0.0289703 ]
 [ 1.72877089  1.01396046]
 [ 1.30990057  1.27469315]
 [ 1.90050772  1.01396046]
 [-0.23991961 -0.0289703 ]
 [ 0.40932938 -0.0289703 ]
 [ 0.47215993 -0.0289703 ]
 [ 0.4302729   2.31762392]]

import pandas
from sklearn import linear_model
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()
df = pandas.read_csv("cars2.csv")
X = df[['Weight', 'Volume']]
y = df['CO2']
scaledX = scale.fit_transform(X)
regr = linear_model.LinearRegression()
regr.fit(scaledX, y)
scaled = scale.transform([[2300, 1.3]])
predictedCO2 = regr.predict([scaled[0]])
print(predictedCO2)

[107.2087328]